Literaturnachweis - Detailanzeige
| Autor/inn/en | Sharma, Kshitij; Chavez-Demoulin, Valérie; Dillenbourg, Pierre |
|---|---|
| Titel | An Application of Extreme Value Theory to Learning Analytics: Predicting Collaboration Outcome from Eye-Tracking Data |
| Quelle | In: Journal of Learning Analytics, 4 (2017) 3, S.140-164 (25 Seiten)Infoseite zur Zeitschrift
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| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| ISSN | 1929-7750 |
| Schlagwörter | Foreign Countries; Educational Research; Statistical Distributions; Theories; Eye Movements; Data Collection; Computer Mediated Communication; Problem Solving; Cooperative Learning; Predictor Variables; Statistical Analysis; Pretests Posttests; Comparative Analysis; Switzerland |
| Abstract | The statistics used in education research are based on central trends such as the mean or standard deviation, discarding outliers. This paper adopts another viewpoint that has emerged in statistics, called extreme value theory (EVT). EVT claims that the bulk of normal distribution is comprised mainly of uninteresting variations while the most extreme values convey more information. We apply EVT to eye-tracking data collected during online collaborative problem solving with the aim of predicting the quality of collaboration. We compare our previous approach, based on central trends, with an EVT approach focused on extreme episodes of collaboration. The latter provided a better prediction of the quality of collaboration. (As Provided). |
| Anmerkungen | Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: http://learning-analytics.info/journals/index.php/JLA/ |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2020/1/01 |
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